Search: a new era
A presentation at BBL Fortis (private event) in in Brussels, Belgium by David Pilato
Search a new era David Pilato | @dadoonet
Elasticsearch You Know, for Search
These are not the droids you are looking for.
GET /_analyze { “char_filter”: [ “html_strip” ], “tokenizer”: “standard”, “filter”: [ “lowercase”, “stop”, “snowball” ], “text”: “These are <em>not</em> the droids you are looking for.” }
“char_filter”: “html_strip” These are <em>not</em> the droids you are looking for. These are not the droids you are looking for.
“tokenizer”: “standard” These are not the droids you are looking for. These are not the droids you are looking for
“filter”: “lowercase” These are not the droids you are looking for these are not the droids you are looking for
“filter”: “stop” These are not the droids you are looking for these are not the droids you are looking for droids you looking
“filter”: “snowball” These are not the droids you are looking for these are not the droids you are looking for droids you droid you looking look
These are <em>not</em> the droids you are looking for. { “tokens”: [{ “token”: “droid”, “start_offset”: 27, “end_offset”: 33, “type”: “<ALPHANUM>”, “position”: 4 },{ “token”: “you”, “start_offset”: 34, “end_offset”: 37, “type”: “<ALPHANUM>”, “position”: 5 }, { “token”: “look”, “start_offset”: 42, “end_offset”: 49, “type”: “<ALPHANUM>”, “position”: 7 }]}
Semantic search ≠ Literal matches
Elasticsearch You Know, for Search
Elasticsearch You Know, for Vector Search
What is a Vector ?
Example: 1-dimensional vector Character Vector [ 1 ] ] Realistic
[ Embeddings represent your data Cartoon 1
represent different data aspects Human Character Vector [ 1, 1 Realistic Cartoon ] ] Machine
[ Multiple dimensions 1, 0
is grouped together Human Character Vector [ 1.0, 1.0 1.0, 0.0 Realistic Cartoon [ 1.0, 0.8 1.0, 1.0 [ 1.0, 1.0 ] ] ] ] ]
Machine
[ [ Similar data
Vector search ranks objects by similarity (~relevance) to the query Human Rank Query 1 Realistic Cartoon 2 3 4 5 Machine Result
How do you index vectors ?
Architecture of Vector Search
dense_vector field type PUT ecommerce { “mappings”: { “properties”: { “description”: { “type”: “text” } “desc_embedding”: { “type”: “dense_vector” } } } }
Data Ingestion and Embedding Generation POST ecommerce/_doc { “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, “fabric”:”cotton” “desc_embedding”:[0.452,0.3242,…], } “desc_embedding”:[0.452,0.3242,…] } “img_embedding”:[0.012,0.0,…] } Source data POST /ecommerce/_doc
With Elastic ML { } Source data { } “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, POST /ecommerce/_doc “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, “desc_embedding”:[0.452,0.3242,…]
How do you search vectors ?
Architecture of Vector Search
knn query GET ecommerce/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “query_vector”: [0.123, 0.244,…] } }], “filter”: { “term”: { “department”: “women” } } } } }, “size”: 10
knn query (with Elastic ML GET ecommerce/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “query_vector_builder”: { “text_embedding”: { “model_text”: “summer clothes”, “model_id”: <text-embedding-model> } } } }], “filter”: { “term”: { “department”: “women” } } } }, “size”: 10 } ) Transformer model
semantic_text field type PUT /_inference/text_embedding/e5-small-multilingual { “service”: “elasticsearch”, “service_settings”: { “num_allocations”: 1, “num_threads”: 1, “model_id”: “.multilingual-e5-small_linux-x86_64” } } POST ecommerce/_doc { “description”: “Our best-selling…” } PUT ecommerce { “mappings”: { “properties”: { “description”: { “type”: “text”, “copy_to”: [ “desc_embedding” ] } “desc_embedding”: { “type”: “semantic_text”, “inference_id”: “e5-small-multilingual” } } } } GET ecommerce/_search { “query”: { “semantic”: { “field”: “desc_embedding” “query” : “I’m looking for a red dress for a DJ party” }}}
Architecture of Vector Search
But how does it really work?
Similarity Human q cos(θ) = d1 d2 Realistic θ q⃗ × d ⃗ | q⃗ | × | d |⃗ _score = 1 + cos(θ) 2
Similarity: cosine (cosine) θ Similar vectors θ close to 0 cos(θ) close to 1 1+1 _score = =1 2 θ Orthogonal vectors θ close to 90° cos(θ) close to 0 1+0 _score = = 0.5 2 θ Opposite vectors θ close to 180° cos(θ) close to -1 1−1 _score = =0 2
https://djdadoo.pilato.fr/
https://github.com/dadoonet/music-search/
Search a new era David Pilato | @dadoonet
Search is not just traditional TF/IDF any more but the current trend of machine learning and models has opened another dimension for search.
This talk gives an overview of:
- “Classic” search and its limitations
- What is a model and how can you use it
- How to use vector search in Elasticsearch
The main demo covers how to generate embeddings from a music and then use the techniques we learned to propose the most probable version of it when we hum a song 🎶🎸🎻.
Resources
The following resources were mentioned during the presentation or are useful additional information.
